Asisstant Professor

Juraj Benić

jbenic@mathos.hr
+385 31 224 835
8 (ground floor)
School of Applied Mathematics and Informatics

Josip Juraj Strossmayer University of Osijek

Research Interests

control theory, IoT, fluid power

Degrees

Publications

Journal Publications

  1. J. Benić, J. Budja, Annotations About Croatian Areal-Diachronic Corpus Building, Čakavska rič : polugodišnjak za proučavanje čakavske riječi 51/1-2 (2024), 181-192
    The paper is about the areal-diachronic corpus of the Croatian language. Special attention is given to the data from the corpora of old texts (before the standardisation of Croatian), which would give a complete overview of and insight into the development of the language. The paper also describes the areal-diachronic sub-corpus consisting of works from the Makarska coast.
  2. M. Krznar, D. Pavković, J. Benić, D. Brezak, M. Cipek, Hybrid Electrical-Internal Combustion Engine Power Supply for Multirotors: Feasibility Analysis and Case Study, Journal of sustainable development of energy, water and environment systems 12/2 (2024)
    Multirotors have proven their significance in fields like aerial imaging, surveying, and inspection. However, their effectiveness is hindered by limited flight autonomy due to the low energy density of standard lithium polymer batteries and high power demand. This necessitates frequent recharging, limiting prolonged operational capabilities. In response, our study introduces a methodology to assess the feasibility of a hybrid internal combustion engine and electric generator set as an alternative power source for multirotors. We investigate three theoretical multirotor designs tailored for mid-to-heavy load capacities (15 – 35 kg max take-off mass) and evaluate both pure battery and hybrid power as vehicle energy sources. Our findings indicate that hybrid power significantly enhances endurance, enabling extended flight times and wider range coverage. This extended aerial presence achieved with the hybrid system's superior endurance over traditional batteries significantly enhances multirotor capabilities, enabling innovative applications and expanded use cases.
  3. J. Benić, D. Brezak, Ž. Šitum, D. Kolar, D. Lisjak, Detailed experimental comparison of energy efficiency between proportional and direct driven hydraulic system, Results in engineering 18 (2023)
    This paper investigates the energy efficiency of a Direct Driven Hydraulic (DDH) system and a proportional electrohydraulic system. A detailed analysis of their energy efficiency is carried out based on experimental results obtained in laboratory conditions using a fully loaded cylinder and sine wave reference trajectory. To ensure fairness in the testing process, the same cylinder with the same initial conditions is used for both systems. The cylinder velocity is estimated online using a novel algebraic differentiator approach based on measured cylinder position. The power of each component is calculated from the obtained measurements, and the energy efficiency of the system is given, along with the losses for each component. Based on the obtained results, it is concluded that the efficiency of the DDH system with a fully loaded cylinder is 28%, while that of the proportional electrohydraulic system is 4%. The significantly higher energy efficiency of the DDH system is attributed to its use of a power-on-demand approach, in contrast to the proportional electrohydraulic system.
  4. J. Benić, A. Penđer, J. Kasać, T. Stipančić, Sugeno-Type Fuzzy Ontology PI Controller for Proportional Electrohydraulic System, IFAC-PapersOnLine 56/2 (2023)
    In this paper, an ontology representation of the Sougeno-type PI Fuzzy Logic Controller (FLC) is presented. The proposed controller is an extension of the Fuzzy Ontology Controller (FOC), where the new fuzzy annotations relevant to Sougeno-type fuzzy control are added to the ontology. The design of the fuzzy ontology is carried out in Protégé and it is hosted on a remote server in the Virtuoso database. Simulation results for the proportional electrohydraulic system are presented and compared to the PI and the SMC controller. The simulation results show that the proposed concept works, and achieves similar results to the PI controller. Experimental results are carried out on a proportional electrohydraulic system. Results show good tracking capabilities despite slow network communication between the server and experimental setup.
  5. D. Kolar, D. Lisjak, M. Curman, J. Benić, Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network - Initial Research, Tehnički glasnik 17/2 (2023), 279-285
    Rotating parts can be found in almost all operational equipment in the industry and are of great importance for proper operation. However, reliability theory explains that every industrial system can change its state when failure happens. Predictive maintenance as one of the latest maintenance strategy emerged from the Maintenance 4.0 concept. Nowadays, this concept can include Industrial Internet of Things (IIoT) devices to connect industrial assets thus enable data collection and analysis that can help make better decisions about maintenance activity. Robust data acquisition system is a prerequisite for any modern predictive maintenance task as it provides necessary data for further analysis and health assessment of the industry asset. Fault diagnosis is an important task in the maintenance of industrial rotating subsystems, considering that early state change diagnosis and fault identification can prevent system failure. Vibration analysis in theory and practice is considered a correct technique for early detection of state changes and failure diagnostics of rotating subsystems. The identified technical state should be considered in a context of the ability and different inability states. Therefore, early different inability states identification is the next step in the rotary machinery diagnostics procedure. Most of the existing techniques for fault diagnosis of rotating subsystems that use vibrations involve the step of extracting features from the raw signal. Considering that the features that describe the behavior of the rotary subsystem can differ significantly depending on the type of equipment, such an approach usually requires an expert in the field of signal processing and rotary subsystems who can define the necessary features. Recently, the emergence of machine deep learning and its application in maintenance promises to provide highly efficient fault diagnostics while simultaneously reducing the need for expert knowledge and human labour. This paper presents authors aim to use self-developed IIoT system built as an IIoT accelerometer as the edge device, web API and database with convolutional neural network as deep learning-based data-driven fault diagnosis to detect and identify different inability states of rotating subsystems. Large dataset for two different rotational speed is collected using IIOT system and multiple convolutional neural network models are trained and tested to examine possibility of using IIOT for inability state prediction.


Professional Activities

Seminars and Talks

  • 21.2.2019. “Upravljanje izravno pogonjenih elektrohidrauličkih sustava”, na seminaru Servohidraulika, Fakultet strojarstva i brodogradnje

Invited Lectures

  • 8.6.2021. “Direct Driven Hydraulic and Internet of Things”, University of Maribor, faculty of Mechanical Engineering

Study Visits Abroad

  • 21.3.2024. – 29.3.2024., M. O. Auezov South Kazakhstan State University, Kazakhstan